. "Area under the ROC Curve by Bubble-Sort Approach (BSA)" . . "Automatic Control, Modeling and Simulation (ACMOS'05)" . "Area under the ROC Curve by Bubble-Sort Approach (BSA)"@en . "A new approach to area under ROC curve (AUC) evaluation is introduced and compared with the current methods. The main idea is based on the Bubble-Sort method. It advantages from the approach to the qualitative dependent variable which is used as the ordinal (and not nominal) variable in comparison to the classical approach. For binary output data the algorithm reaches the same complexity and values as the other methods. For multi-class classification the complexity differs from the classical approaches and the BSA values of AUC don't fail in the special cases as the current methods do." . . . "[CBF8B0D41B0A]" . . "Honz\u00EDk, Petr" . . . "494-499" . "26220" . . . "Area under the ROC Curve by Bubble-Sort Approach (BSA)" . . . "1"^^ . "V \u010Dl\u00E1nku je p\u0159edstavena nov\u00E1 metoda pro v\u00FDpo\u010Det plochy pod ROC (AUC) a srovn\u00E1na se st\u00E1vaj\u00EDc\u00EDmi metodami. Z\u00E1kladn\u00ED princip nov\u00E9 metody je zalo\u017Een na algoritmu \u0159azen\u00ED Bubble-sort. Nov\u00FD algoritmus m\u00E1 ni\u017E\u0161\u00ED komplexnost ne\u017E sou\u010Dasn\u00E9 postupy p\u0159i v\u00FDpo\u010Dtu AUC pro klasifikaci do v\u00EDce ne\u017E dvou t\u0159\u00EDd."@cs . "6"^^ . "2005-03-13+01:00"^^ . "RIV/00216305:26220/05:PU47669" . . . "Praha" . "1"^^ . . "Praha" . "513052" . . "V\u00FDpo\u010Det plochy pod k\u0159ivkou ROC metodou Bubble-Sort (BSA)"@cs . "RIV/00216305:26220/05:PU47669!RIV06-GA0-26220___" . . "V\u00FDpo\u010Det plochy pod k\u0159ivkou ROC metodou Bubble-Sort (BSA)"@cs . . . "Area under the ROC Curve by Bubble-Sort Approach (BSA)"@en . . "ROC, AUC, AUC evaluation, computational complexity, classification"@en . "P(GA102/03/1097), P(GA102/05/0663)" . "A new approach to area under ROC curve (AUC) evaluation is introduced and compared with the current methods. The main idea is based on the Bubble-Sort method. It advantages from the approach to the qualitative dependent variable which is used as the ordinal (and not nominal) variable in comparison to the classical approach. For binary output data the algorithm reaches the same complexity and values as the other methods. For multi-class classification the complexity differs from the classical approaches and the BSA values of AUC don't fail in the special cases as the current methods do."@en . "960-8457-12-2" . "WSEAS" .